Information recommendation method and device, and storage medium

ABSTRACT

An information recommendation method and device and a storage medium. The information recommendation method includes: determining a target recommendation parameter corresponding to a page identifier of a page, according to the page identifier and a correspondence between a page identifier and a recommendation parameter; determining a corresponding target recommendation strategy according to the target recommendation parameter; querying a correspondence between a recommendation strategy and a recommendation result according to the target recommendation strategy, so as to obtain at least one initial recommendation result; and fusing the at least one initial recommendation result according to a corresponding weight to obtain a target recommendation result.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to Chinese Patent ApplicationNo. 201910033469.8, filed on Jan. 14, 2019, the disclosure of which isincorporated herein by reference in its entirety as part of the presentapplication.

TECHNICAL FIELD

Embodiments of the present disclosure relate to an informationrecommendation method and device, and a storage medium.

BACKGROUND

With the development of an information technology and the Internet, thehuman society is developed from an information shortage era to aninformation overload era. It becomes increasingly difficult for aninformation consumer to find information of interest from a large amountof information and for an information producer to make producedinformation stand out from a lot of information.

In related art, information may be recommended to a user when the userbrowses information, so as to assist the user in finding information ofinterest quickly. However, how to improve pertinence of informationrecommendation is a technical problem to be solved.

SUMMARY

At least one embodiment of the present disclosure provides aninformation recommendation method, which includes:

determining a target recommendation parameter corresponding to a pageidentifier of a page, according to the page identifier and acorrespondence between a page identifier and a recommendation parameter;

determining a corresponding target recommendation strategy according tothe target recommendation parameter;

querying a correspondence between a recommendation strategy and arecommendation result according to the target recommendation strategy,so as to obtain at least one initial recommendation result; and

fusing the at least one initial recommendation result according to acorresponding weight to obtain a target recommendation result.

In an embodiment, the page is a first recommendation page; and thetarget recommendation parameter is a user identifier of a user.

In an embodiment, the target recommendation strategy is a firstrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a first initial recommendation result, a second initialrecommendation result and a third initial recommendation result,according to the first recommendation strategy;

wherein the first initial recommendation result is a target recommendedto the user according to target preference data of the usercorresponding to the user identifier;

the second initial recommendation result is a target recommended to theuser according to a tag of the user corresponding to the useridentifier; and

the third initial recommendation result is a target recommended to theuser according to a put-on-sale time of the target and the useridentifier, and the put-on-sale time meets a preset condition.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that user-targetinteraction behavior data corresponding to the user identifier exists ina preset database, and

the target preference data is obtained according to the user-targetinteraction behavior data corresponding to the user identifier.

In an embodiment, the target recommendation strategy is a secondrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a second initial recommendation result and a third initialrecommendation result, according to the second recommendation strategy;

wherein the second initial recommendation result is a target recommendedto the user according to a tag of the user corresponding to the useridentifier; and

the third initial recommendation result is a target recommended to theuser according to a put-on-sale time of the target and the useridentifier, and the put-on-sale time meets a preset condition.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that user-targetinteraction behavior data corresponding to the user identifier is absentin a preset database.

In an embodiment, the page is a second recommendation page; and thetarget recommendation parameter comprises a target identifier and a useridentifier of a user.

In an embodiment, the target recommendation strategy is a thirdrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a first initial recommendation result, a fourth initialrecommendation result and a fifth initial recommendation result,according to the third recommendation strategy;

wherein the first initial recommendation result is a target recommendedto the user according to target preference data of the usercorresponding to the user identifier;

the fourth initial recommendation result is a target recommended to theuser according to a first correspondence between the target identifierand a target identifier of a similar target, and the target preferencedata and the first correspondence are obtained according to user-targetinteraction behavior data corresponding to the user identifier; and

the fifth initial recommendation result is a target recommended to theuser according to the target identifier and a second correspondencebetween the target identifier and a target identifier of a similartarget, and the second correspondence is obtained by calculating asimilarity between targets according to attribute data of the targets.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that the user-targetinteraction behavior data corresponding to the user identifier exists ina preset database; and

determining according to the target identifier that target interactionbehavior data corresponding to the target identifier exists in a presetdatabase.

In an embodiment, the target recommendation strategy is a fourthrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a first initial recommendation result and a fifth initialrecommendation result, according to the fourth recommendation strategy;

wherein the first initial recommendation result is a target recommendedto the user according to target preference data of the user, and thetarget preference data is obtained by inputting user-target interactionbehavior data into a trained recommendation model; and

the fifth initial recommendation result is a target recommended to theuser according to the target identifier and a second correspondencebetween the target identifier and a target identifier of a similartarget, and the second correspondence is obtained by calculating asimilarity between targets according to attribute data of the targets.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that the user-targetinteraction behavior data corresponding to the user identifier exists ina preset database; and

determining according to the target identifier that target interactionbehavior data corresponding to the target identifier is absent in apreset database.

In an embodiment, the target recommendation strategy is a fifthrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a fourth initial recommendation result and a fifth initialrecommendation result, according to the fifth recommendation strategy;

wherein the fourth initial recommendation result is the targetrecommended to the user according to a first correspondence between thetarget identifier and a target identifier of a similar target, and thefirst correspondence is obtained according to user-target interactionbehavior data corresponding to the user identifier; and

the fifth initial recommendation result is a target recommended to theuser according to the target identifier and a second correspondencebetween the target identifier and a target identifier of a similartarget, and the second correspondence is obtained by calculating asimilarity between targets according to attribute data of the targets.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that user-targetinteraction behavior data corresponding to the user identifier is absentin a preset database; and

determining according to the target identifier that target interactionbehavior data corresponding to the target identifier exists in a presetdatabase.

In an embodiment, the target recommendation strategy is a sixthrecommendation strategy;

the querying the correspondence between the recommendation strategy andthe recommendation result according to the target recommendationstrategy, so as to obtain the at least one initial recommendation resultcomprises:

obtaining a fifth initial recommendation result according to the sixthrecommendation strategy;

wherein the fifth initial recommendation result is a target recommendedto the user according to the target identifier and a secondcorrespondence between the target identifier and a target identifier ofa similar target, and the second correspondence is obtained bycalculating a similarity between targets according to attribute data ofthe targets.

In an embodiment, prior to determining the corresponding targetrecommendation strategy according to the target recommendationparameter, the information recommendation method further comprises:

determining according to the user identifier that user-targetinteraction behavior data corresponding to the user identifier is absentin a preset database; and

determining according to the target identifier that target interactionbehavior data corresponding to the target identifier is absent in apreset database.

In an embodiment, the user-target interaction behavior data comprises atleast one of a group consisting of target purchase behavior data, targetcommenting behavior data, target sharing behavior data, targetcollecting behavior data, target likes-giving behavior data, targetbrowsing behavior data and target pushing behavior data.

In an embodiment, the target interaction behavior data comprises atleast one of a group consisting of target purchase behavior data, targetcommenting behavior data, target sharing behavior data, targetcollecting behavior data, target likes-giving behavior data, targetbrowsing behavior data and target pushing behavior data.

In an embodiment, obtaining at least one initial recommendation resultaccording to the target recommendation strategy comprises:

obtaining the at least one initial recommendation result from a databaseaccording to the target recommendation strategy, wherein the at leastone initial recommendation result is stored in the database in advance.

At least one embodiment of the present disclosure further provides aninformation recommendation device, which includes:

a first determining module configured to determine a targetrecommendation parameter corresponding to a page identifier of a page,according to the page identifier and a correspondence between a pageidentifier and a recommendation parameter;

a second determining module configured to determine a correspondingtarget recommendation strategy according to the target recommendationparameter;

a querying module configured to query a correspondence between arecommendation strategy and a recommendation result according to thetarget recommendation strategy, so as to obtain at least one initialrecommendation result; and

a fusing module configured to fuse the at least one initialrecommendation result according to a corresponding weight to obtain atarget recommendation result.

At least one embodiment of the present disclosure further provides aninformation recommendation device, which includes:

a processor; and

a memory,

wherein the memory is configured to store instructions, and theinstructions, when executed by the processor, cause the processor toexecute operations comprising:

determining a target recommendation parameter corresponding to a pageidentifier of a page, according to the page identifier and acorrespondence between a page identifier and a recommendation parameter;

determining a corresponding target recommendation strategy according tothe target recommendation parameter;

querying a correspondence between a recommendation strategy and arecommendation result according to the target recommendation strategy,so as to obtain at least one initial recommendation result; and

fusing the at least one initial recommendation result according to acorresponding weight to obtain a target recommendation result.

At least one embodiment of the present disclosure further provides anon-transitory computer storage medium configured to store instructions,the instructions, when executed by a processor, causing the processor toexecute operations comprising:

determining a target recommendation parameter corresponding to a pageidentifier of a page, according to the page identifier and acorrespondence between a page identifier and a recommendation parameter;

determining a corresponding target recommendation strategy according tothe target recommendation parameter;

querying a correspondence between a recommendation strategy and arecommendation result according to the target recommendation strategy,so as to obtain at least one initial recommendation result; and

fusing the at least one initial recommendation result according to acorresponding weight to obtain a target recommendation result.

It should be understood that the above general description and thefollowing detailed description are only illustrative and explanatory,and cannot be construed to limit the embodiments of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to clearly illustrate the technical solution of the embodimentsof the present disclosure, the drawings of the embodiments will bebriefly described in the following; it is obvious that the describeddrawings are only related to some embodiments of the present disclosureand thus are not limitative of the present disclosure.

FIG. 1 is a schematic structural diagram of a recommendation systemaccording to at least one embodiment of the present disclosure;

FIG. 2 is a flow chart of an information recommendation method accordingto at least one embodiment of the present disclosure;

FIG. 3 is a block diagram of an information recommendation deviceaccording to at least one embodiment of the present disclosure; and

FIG. 4 is a block diagram of an information recommendation deviceaccording to at least one embodiment of the present disclosure.

DETAILED DESCRIPTION

In order to make objects, technical details and advantages of theembodiments of the present disclosure apparent, the technical solutionsof the embodiments will be described in a clearly and fullyunderstandable way in connection with the drawings related to theembodiments of the present disclosure. Apparently, the describedembodiments are just a part but not all of the embodiments of thepresent disclosure. Based on the described embodiments herein, thoseskilled in the art can obtain other embodiment(s), without any inventivework, which should be within the scope of the present disclosure.

Hereinafter, embodiments of the present disclosure will be described bytaking recommendation of a commodity to a user as an example. However,it should be understood that in other embodiments, in addition tocommodities, targets recommended to the user may include, for example,news, videos, music, paintings, etc., which is not limited in theembodiments of the present disclosure.

At least one embodiment of the present disclosure provides aninformation recommendation method which may be applied to arecommendation system as shown in FIG. 1. The recommendation system maybe applied to a news website, a news application, a shopping website, ashopping application, a video website, a music application, etc., whichis not limited in the embodiments of the present disclosure. Beforedescribing the information recommendation method according to theembodiments of the present disclosure, the recommendation system shownin FIG. 1 will be described below. It should be understood that therecommendation system shown in FIG. 1 is only an example, and theinformation recommendation method according to the present disclosuremay also be applied to a recommendation system providing other results,which is not limited in the embodiments of the present disclosure.

In an embodiment, as shown in FIG. 1, the recommendation system mayinclude an offline layer, an online layer and a user interface (UI)layer. The offline layer is used to store data, train a recommendationmodel with the stored data to obtain a trained recommendation model,obtain at least one initial recommendation result by using the storeddata, the trained recommendation model and a preset algorithm, andoutput the obtained at least one initial recommendation result to theonline layer for storage. The online layer is used to store the at leastone initial recommendation result, and further to determine acorresponding target recommendation parameter according to a currentpage displayed by the UI layer, determine a corresponding targetrecommendation strategy according to the target recommendationparameter, acquire corresponding at least one initial recommendationresult from the stored at least one initial recommendation resultaccording to the target recommendation strategy, and fuse the acquiredat least one initial recommendation result according to a correspondingweight to obtain a target recommendation result. The online layer isalso used to output the target recommendation result to the UI layer tobe displayed to the user.

In an embodiment, the data stored in the offline layer may be updatedaccording to a preset period based on data stored in a businessdatabase. The business database may be created in a server of therecommendation system. Business data may be stored in the businessdatabase, and include user data, commodity attribute data,user-commodity interaction behavior data and commodity interactionbehavior data. The user-commodity interaction behavior data may includeat least one of commodity purchase behavior data, commodity commentingbehavior data, commodity sharing behavior data, commodity collectingbehavior data, commodity likes-giving behavior data, commodity browsingbehavior data and commodity pushing behavior data. The commodityinteraction behavior data may include at least one of commodity purchasebehavior data, commodity commenting behavior data, commodity sharingbehavior data, commodity collecting behavior data, commoditylikes-giving behavior data, commodity browsing behavior data andcommodity pushing behavior data. For example, the commodity purchasebehavior data may be saved in an order table, and the commodity sharingbehavior data may be saved in a sharing table. The user data may includeuser tags which may be saved in a user tag table. The commodityattribute data may include commodity tags which may be saved in acommodity tag table.

In an embodiment, a Hadoop platform may be adopted as the offline layer,and the data may be stored with a Hadoop Distributed File System (HDFS)in the Hadoop platform. After imported into the Hadoop platform from thebusiness database, the data may be summarized by a data summarizationmodule. Specifically, one database table may be stored in one folder, aplurality of files are arranged in the folder for storing the data in atext file with commas as separators, and the storage folders of all thedatabase tables may be stored in a general folder.

In the offline layer, before summarization of the data, operations ofscreening, deduplication, optimization, etc., may also be performed onthe data, which is not limited in the embodiments of the presentdisclosure.

It should be understood that in other embodiments, the offline layer mayalso be implemented using other types of platforms (for example,non-distributed storage platforms), which is not limited in theembodiments of the present disclosure.

In an embodiment, the data imported from the business database may beprocessed by the offline layer using a preset algorithm and the trainedrecommendation model, so as to obtain the at least one initialrecommendation result. In an exemplary embodiment, the at least oneinitial recommendation result may include a first initial recommendationresult, a second initial recommendation result, a third initialrecommendation result, a fourth initial recommendation result and afifth initial recommendation result. The first initial recommendationresult is a commodity recommended to the user according to commoditypreference data of the user. The second initial recommendation result isa commodity recommended to the user according to the tag of the user anda correspondence between the commodity and the tag. The third initialrecommendation result is a commodity recommended to the user accordingto the put-on-sale time of the commodity and a user identifier andhaving a put-on-sale time meeting a preset condition. The fourth initialrecommendation result is a commodity recommended to the user accordingto a first correspondence between a commodity identifier and a commodityidentifier of a similar commodity. The fifth initial recommendationresult is a commodity recommended to the user according to a commodityidentifier and a second correspondence between the commodity identifierand a commodity identifier of a similar commodity, and the secondcorrespondence is obtained by calculating a similarity between thecommodities according to the commodity attribute data. The commoditypreference data and the first correspondence are obtained by inputtingthe user-commodity interaction behavior data into the trainedrecommendation model.

A method of obtaining the first and fourth initial recommendationresults by an offline calculation module in the offline layer using thetrained recommendation model is described below. The user-commodityinteraction behavior data is input into and processed by the trainedrecommendation model to obtain a preference value of each user for eachcommodity and the similarity between the commodities, and the preferencevalue of each user for each commodity is the commodity preference dataof the user. Then, the first initial recommendation result of thecommodity recommended to the user may be generated according to thecommodity preference data of the user. Meanwhile, the firstcorrespondence between a commodity identifier and the commodityidentifier of a similar commodity may be obtained according to thesimilarity between the commodities, and the fourth initialrecommendation result of the commodity recommended to the user may beobtained according to the first correspondence.

A training method of the recommendation model is described below. Therecommendation model may be trained by a model training module in theoffline layer using a part of the stored data as a training set and averification set, so as to obtain the trained recommendation model. Inan embodiment, the recommendation model may be based on a collaborativefiltering recommendation algorithm. In this embodiment, theuser-commodity interaction behavior data may be read from the Hadoopplatform, and preprocessed to obtain pure user-commodity interactionbehavior data which is then synthesized, subjected to format conversion,and deduplicated to obtain deduplicated user-commodity interactionbehavior data. Then, the deduplicated user-commodity interactionbehavior data is divided into a training set, a verification set and atest set according to a time stamp, but the division of the data sets isnot limited thereto. Then, the recommendation model based on thecollaborative filtering recommendation algorithm is trained with thetraining set and the verification set to determine hyperparameters ofthe recommendation model, so as to obtain a trained recommendation modelbased on the collaborative filtering recommendation algorithm. Thehyperparameters are parameters set before the recommendation model istrained, rather than parameters obtained by the training process.

In an exemplary embodiment, the user-commodity interaction behavior datamay be a score matrix R of the user for the commodity. In the process oftraining the recommendation model based on the collaborative filteringrecommendation algorithm, the score matrix R may be decomposed into twolow-dimensional matrices p, q, the matrix p is a factor matrix of theuser, and the matrix q is a factor matrix of the commodity. In thematrix p, each matrix element is the preference value of the user forthe commodity, each row corresponds to one user, and each columncorresponds to a hidden attribute (latent factor). The hidden attributemay have no actual or specific meaning and no interpretability, and isused for describing an attribute of the commodity. In the matrix q, eachmatrix element is a weight value of the commodity, each row correspondsto one commodity, and each column corresponds to a hidden attribute(latch factor). An unknown score in the score matrix R may be calculatedby multiplying the two low-dimensional matrices p, q. The product of thetwo low-dimensional matrices p, q may be represented by {circumflex over(R)}, and the score matrix R is approximately equal to {circumflex over(R)}. A relationship between the two low-dimensional matrices p, q, thescore matrix R and {circumflex over (R)} may be seen in the followingformula (1):

R≈{circumflex over (R)}=p ^(T) q  (1)

In the above-mentioned exemplary embodiment, the matrix may bedecomposed by solving the following loss function (2):

$\begin{matrix}{{\min \mspace{14mu} {C\left( {p,q} \right)}} = {{\min \; {\sum\limits_{{({u,i})} \in {Train}}\left( {r_{ui} - {\sum\limits_{f = 1}^{F}{p_{uf}q_{if}}}} \right)^{2}}} + {\lambda \left( {{p_{u}}^{2} + {q_{i}}^{2}} \right)}}} & (2)\end{matrix}$

where u is the user identifier, i is the commodity identifier, r_(ui) isthe known score of the user u for the commodity i, p and q represent thefactor matrices of the user and the commodity respectively, whichrepresent values of each user and each commodity on each feature of thecorresponding factor matrix respectively, f is the number of columns ofthe matrices p, q, F is the total number of the columns of the matricesp, q, i.e., the total number of the features, and Train is the trainingset. A second term in the loss function (2) is a regularization term, λis a coefficient before the regularization term, and the regularizationterm is added into the loss function to prevent overfitting and controlthe complexity of the model. The more complex the model is, the largerthe regularization value is, and λ is greater than or equal to 0.

In the above-mentioned exemplary embodiment, optimal solutions p, q,i.e., the decomposed low-dimensional matrices, may be calculated with astochastic gradient descent method or an alternating least squares (ALS)method. After the low-dimensional matrices p, q are obtained, aprediction score of the user u for the commodity j, i.e., the preferencevalue of the user u for the commodity j, may be obtained with thefollowing formula (3), and a value of the similarity between thecommodities i, j may be obtained with the following formula (4):

{circumflex over (r)} _(uj) =p _(u) ^(T) q _(j)  (3)

w _(ij) =q _(i) q _(j)  (4)

In the above-mentioned exemplary embodiment, an accuracy rate and arecall rate may be calculated with the test set to determine whether therecommendation model meets requirements. The accuracy rate is aproportion that the commodities with interaction behaviors recommendedto the user in the test set account for in all the commodities withinteraction behaviors, and the recall rate is a proportion that thecommodities with interaction behaviors recommended to the user in thetest set account for in all the recommendation results.

In the above-mentioned exemplary embodiment, the trained recommendationmodel is obtained after the recommendation model is determined to meetrequirements. The commodity preference data of the user may be obtainedusing the trained recommendation model and the above-mentioned formula(3), and the first initial recommendation result of the commodityrecommended to the user may be generated according to the commoditypreference data of the user. The first correspondence between thecommodity identifier of the and the commodity of the similar commoditymay be obtained using the trained recommendation model and theabove-mentioned formula (4), and the fourth initial recommendationresult of the commodity recommended to the user may be generatedaccording to the first correspondence between the commodity identifierand the commodity identifier of the similar commodity.

A method of obtaining the second initial recommendation result by usinga tag-based recommendation algorithm is described below. First, partialattribute data of the commodity may be extracted from the attribute dataof the commodity in a preset commodity database. When some attributedata of the commodity is extracted from the attribute data of thecommodity, the attribute data of the commodity with a specified tag maybe extracted randomly, or the partial attribute data of the commoditymay be extracted according to other data extraction methods. Then, thecommodities purchased by each user are counted. Next, for each user, thepurchased commodities are filtered out from the commodity database toobtain a filtered commodity database. Then, for each user, the filteredcommodity database are searched for the commodities with the commoditytags completely or partially identical to the tag of the user accordingto the tag of the user, so as to obtain a first commodity set. Then, foreach user, the commodity recommended to the user is extracted from thefirst commodity set to obtain the second initial recommendation result.When the commodity recommended to the user is extracted from the firstcommodity set, a specified number of commodities may be extractedrandomly, or the commodity may be extracted according to other dataextraction methods.

A method of obtaining the third initial recommendation result by using anew-commodity-based recommendation algorithm is described below. A newcommodity has a time interval between the put-on-sale time and thecurrent time below a preset threshold. First, the commodities with theput-on-sale time meeting a preset condition are extracted from theattribute data of the commodities according to the put-on-sale time ofthe commodities, so as to obtain a second commodity set. The attributedata of the commodities includes the put-on-sale time. The presetcondition may be that the time interval between the put-on-sale time andthe current time is below the preset threshold. Then, the commoditiespurchased by each user are counted. Next, for each user, the purchasedcommodities are filtered out from the second commodity set to obtain athird commodity set. Then, for each user, the commodity recommended tothe user is extracted from the third commodity set to obtain the thirdinitial recommendation result. When the commodity recommended to theuser is extracted from the third commodity set, a specified number ofcommodities may be extracted randomly, or the commodity may be extractedaccording to other data extraction methods.

A method of obtaining the fifth initial recommendation result by using acontent-based recommendation algorithm is described below. First, theattribute data of each commodity may be converted into a vector M. In anexemplary embodiment, a multi-hot conversion may be performed on theattribute data of each commodity to obtain the vector M. That is,multiple values of a single feature are converted into the vector M, aposition including a feature value has a value of 1, and other positionshave a value of 0. In an exemplary embodiment, the commodity may be apainting, a movie, a book, or the like. The attribute data of thecommodity may include subject data and type data thereof. Then, thesimilarity between the commodities is calculated according to the vectorcorresponding to each commodity. In an exemplary embodiment, thesimilarity between the commodities may be calculated using the Jaccardsimilarity coefficient algorithm. For example, w_(ij) is the similaritybetween the commodities i, j, and may be calculated by the followingformula (5). In the Jaccard similarity coefficient algorithm, only setoperation is performed, numerical values are ignored, and the data onlyincludes 0 and 1, with a calculation efficiency which is relativelyhigh. Then, for each commodity, a specified number of commodities withthe highest similarity are taken as the recommendation result, i.e., thefifth initial recommendation result.

w _(ij) =M _(i) ·M _(j)  (5)

In the above-mentioned exemplary embodiment, the above-mentioned first,second, third, fourth and fifth initial recommendation results may beoutput to the online layer by the offline layer for storage. In anexemplary embodiment, the first, second, third, fourth and fifth initialrecommendation results received from the offline layer may be stored byusing a remote dictionary server (Redis) storage system of the onlinelayer. The received data is stored in the Redis storage system in akey-value format. For example, in the fifth initial recommendationresult, key is the commodity identifier of the commodity, and value is aset of the commodity identifiers of the commodities in therecommendation result. For example, the Redis storage system includes aRedis database.

It should be understood that in other embodiments, at least one of thefirst, second, third, fourth and fifth initial recommendation resultsmay also be stored by using other types of databases, which is notlimited in the embodiments of the present disclosure.

In an embodiment, the online layer includes an online service modulewhich is used to provide online services. For example, the onlineservice module may determine the corresponding target recommendationparameter according to the current page displayed by the UI layer,determine the corresponding target recommendation strategy according tothe target recommendation parameter, acquire the corresponding at leastone initial recommendation result from the stored at least one initialrecommendation result according to the target recommendation strategy,and fuse the acquired at least one initial recommendation resultaccording to the corresponding weight to obtain the targetrecommendation result. The online layer is also used to output thetarget recommendation result to the UI layer. The UI layer may outputthe target recommendation result, for example, display the targetrecommendation result in a preset area in the current page.

The recommendation system according to the embodiments of the presentdisclosure has been described above, and the information recommendationmethod according to the embodiments of the present disclosure isdescribed below. The information recommendation method may be applied toa terminal equipment which may be a server, for example, or to a systemincluding a server and a client as well. The following description ismade by taking applying the information recommendation method to aserver as an example. As shown in FIG. 2, the information recommendationmethod may include the following steps 201-204.

Step 201: determining a target recommendation parameter corresponding toa page identifier of a page, according to the page identifier and acorrespondence between the page identifier and recommendationparameters.

In an embodiment, the page may be a first recommendation page or asecond recommendation page. The first and second recommendation pagescorrespond to different recommendation parameters respectively. Thefirst recommendation page corresponds to the recommendation parameterwhich is a user identifier, and the recommendation parameter of thesecond recommendation page includes a user identifier and a commodityidentifier. The correspondence between the page identifier and therecommendation parameter may be stored in the server in advance. In anembodiment, each page for displaying information corresponds to a pageidentifier. When a user browses the information at the page, the targetrecommendation parameter corresponding to the page identifier of thepage may be determined according to the page identifier and thecorrespondence between the page identifier and the recommendationparameter.

In an exemplary scenario, the information recommendation methodaccording to the embodiments of the present disclosure is applied to apainting application. The painting application is application softwarefor selling paintings and may provide a first recommendation page and asecond recommendation page. The first recommendation page may display atleast one recommended painting. The second recommendation page maydisplay detailed information of the painting, for example, the number of“likes”, a comment, a price, a name, a brief introduction, a tag, etc.The page identifier of the first recommendation page may be P01, and thepage identifier of the second recommendation page may be P02.

Continuing with the above-mentioned exemplary scenario, thecorrespondence between the page identifier and the recommendationparameter stored in the server in advance may be shown in table 1 below.When the page identifier of the current page is P01, the table 1 islooked up according to P01, and the target recommendation parameter isthe user identifier.

TABLE 1 Page Identifier Recommendation Parameter P01 User Identifier P02User Identifier and Commodity Identifier

Step 202: determining a corresponding target recommendation strategyaccording to the target recommendation parameter.

In an exemplary embodiment, in the case where the target recommendationparameter is the user identifier, if user-commodity interaction behaviordata corresponding to the user identifier exists in a database preset inthe server, a first recommendation strategy is determined as thecorresponding target recommendation strategy. In the case where thetarget recommendation parameter is the user identifier, if theuser-commodity interaction behavior data corresponding to the useridentifier does not exist in the database preset in the server, a secondrecommendation strategy is determined as the corresponding targetrecommendation strategy.

In another exemplary embodiment, the target recommendation parameterincludes the user identifier and the commodity identifier. In the casewhere the user-commodity interaction behavior data corresponding to theuser identifier and the commodity interaction behavior datacorresponding to the commodity identifier exist in the preset database,a third recommendation strategy is determined as the correspondingtarget recommendation strategy. In the case where the user-commodityinteraction behavior data corresponding to the user identifier exists inthe preset database and the commodity interaction behavior datacorresponding to the commodity identifier does not exist in the presetdatabase, a fourth recommendation strategy is determined as thecorresponding target recommendation strategy. When the user-commodityinteraction behavior data corresponding to the user identifier does notexist in the preset database and the commodity interaction behavior datacorresponding to the commodity identifier exists in the preset database,a fifth recommendation strategy is determined as the correspondingtarget recommendation strategy. When the user-commodity interactionbehavior data corresponding to the user identifier and the commodityinteraction behavior data corresponding to the commodity identifier donot exist in the preset database, a sixth recommendation strategy isdetermined as the corresponding target recommendation strategy.

In an embodiment, the current page is the first recommendation page, thetarget recommendation parameter is the user identifier, and acorrespondence between the user identifier and the user-commodityinteraction behavior data is stored in the database. In this embodiment,before the step 202, if the user-commodity interaction behavior datacorresponding to the user identifier is determined to exist in thepreset database according to the user identifier, the firstrecommendation strategy is determined as the corresponding targetrecommendation strategy.

Before the step 202, if the user-commodity interaction behavior datacorresponding to the user identifier is determined not to exist in thepreset database according to the user identifier, the secondrecommendation strategy is determined as the corresponding targetrecommendation strategy.

In an embodiment, the current page is the second recommendation page,the target recommendation parameter includes the user identifier and thecommodity identifier, and the correspondence between the user identifierand the user-commodity interaction behavior data as well as acorrespondence between the commodity identifier and the commodityinteraction behavior data are stored in the database. In thisembodiment, before the step 202, if the user-commodity interactionbehavior data corresponding to the user identifier is determined toexist in the preset database according to the user identifier, and thecommodity interaction behavior data corresponding to the commodityidentifier is determined to exist in the preset database according tothe commodity identifier, the third recommendation strategy isdetermined as the corresponding target recommendation strategy.

Before the step 202, if the user-commodity interaction behavior datacorresponding to the user identifier is determined to exist in thepreset database according to the user identifier, and the commodityinteraction behavior data corresponding to the commodity identifier isdetermined not to exist in the preset database according to thecommodity identifier, the fourth recommendation strategy is determinedas the corresponding target recommendation strategy.

Before the step 202, if the user-commodity interaction behavior datacorresponding to the user identifier is determined not to exist in thepreset database according to the user identifier, and the commodityinteraction behavior data corresponding to the commodity identifier isdetermined to exist in the preset database according to the commodityidentifier, the fifth recommendation strategy is determined as thecorresponding target recommendation strategy.

Before the step 202, if the user-commodity interaction behavior datacorresponding to the user identifier is determined not to exist in thepreset database according to the user identifier, and the commodityinteraction behavior data corresponding to the commodity identifier isdetermined not to exist in the preset database according to thecommodity identifier, the sixth recommendation strategy is determined asthe corresponding target recommendation strategy.

TABLE 2 Recommendation Strategy Recommendation Result FirstRecommendation First, Second and Third Initial Strategy RecommendationResults Second Recommendation Second and Third Initial RecommendationStrategy Results Third Recommendation First, Fourth and Fifth InitialStrategy Recommendation Results Fourth Recommendation First and FifthInitial Recommendation Strategy Results Fifth Recommendation Fourth andFifth Initial Recommendation Strategy Results Sixth Recommendation FifthInitial Recommendation Result Strategy

Step 203: obtaining at least one initial recommendation result accordingto the target recommendation strategy.

In an embodiment, a correspondence between the recommendation strategyand the recommendation result may be stored in the server in advance andis shown in table 2. The corresponding at least one initialrecommendation result may be obtained by the server looking up the table2 according to the target recommendation strategy. For example, in thecase where the first recommendation strategy is the targetrecommendation strategy, the table 2 may be looked up to obtain a firstinitial recommendation result, a second initial recommendation resultand a third initial recommendation result.

In the case where the fifth recommendation strategy is the targetrecommendation strategy, a fourth initial recommendation result and afifth initial recommendation result are obtained. In this case, a methodof obtaining the fourth initial recommendation result is substantiallythe same as the above-mentioned method of obtaining the fourth initialrecommendation result, except that the score matrix R of the user forthe commodity is preset.

In some embodiments, before the step 203, the information recommendationmethod may further include obtaining the at least one initialrecommendation result from a database in which the at least one initialrecommendation result is stored in advance according to the targetrecommendation strategy.

Step 204: fusing the at least one initial recommendation resultaccording to a corresponding weight to obtain a target recommendationresult.

In an embodiment, each initial recommendation result has a correspondingweight. A correspondence between the initial recommendation results andthe weights may be stored in the server in advance and shown in table 3below. The table 3 may be looked up by the server according to theinitial recommendation result to obtain the corresponding weight. Forexample, the table 3 is looked up according to the fifth initialrecommendation result to obtain the weight C5.

TABLE 3 Initial Recommendation Result Weight First InitialRecommendation Result C1 Second Initial Recommendation Result C2 ThirdInitial Recommendation Result C3 Fourth Initial Recommendation Result C4Fifth Initial Recommendation Result C5

In an embodiment, the at least one initial recommendation result may befused according to the corresponding weight to obtain the targetrecommendation result. In an exemplary embodiment, in the case where thefirst recommendation strategy is the target recommendation strategy, thetable 2 may be looked up to obtain the first, second and third initialrecommendation results, the table 3 may be then looked up to obtain theweights C1, C2 and C3 corresponding to the first, second and thirdinitial recommendation results respectively, and then, the first, secondand third initial recommendation results may be fused according to thecorresponding weights C1, C2 and C3 to obtain the target recommendationresult.

In an exemplary embodiment, the first initial recommendation result mayinclude commodities 1, 2 and 3, the second initial recommendation resultmay include commodities 1 and 2, the third initial recommendation resultmay include commodities 1, 3 and 4, C1, C2 and C3 are 0.3, 0.2 and 0.2respectively, and then, the weights of commodities 1, 2, 3 and 4obtained after the fusion of the recommendation results are 0.7, 0.5,0.5 and 0.2 respectively. Then, the fused recommendation results may besorted, and the specified number of commodities with the highest weightsare taken as the target recommendation result. For example, threecommodities (commodities 1, 2 and 3) with the highest weights may betaken as the target recommendation result.

In another exemplary embodiment, when the second recommendation strategyis the target recommendation strategy, the table 2 may be looked up toobtain the second and third initial recommendation results, the table 3may be then looked up to obtain the weights C2 and C3 corresponding tothe second and third initial recommendation results respectively, andthen, the second and third initial recommendation results may be fusedaccording to the corresponding weights C2 and C3 to obtain the targetrecommendation result.

In another exemplary embodiment, when the third recommendation strategyis the target recommendation strategy, the table 2 may be looked up toobtain the first, fourth and fifth initial recommendation results, thetable 3 may be then looked up to obtain the weights C1, C4 and C5corresponding to the first, fourth and fifth initial recommendationresults respectively, and then, the first, fourth and fifth initialrecommendation results may be fused according to the correspondingweights C1, C4 and C5 to obtain the target recommendation result.

In another exemplary embodiment, when the fourth recommendation strategyis the target recommendation strategy, the table 2 may be looked up toobtain the first and fifth initial recommendation results, the table 3may be then looked up to obtain the weights C1 and C5 corresponding tothe first and fifth initial recommendation results respectively, andthen, the first and fifth initial recommendation results may be fusedaccording to the corresponding weights C1 and C5 to obtain the targetrecommendation result.

In another exemplary embodiment, when the fifth recommendation strategyis the target recommendation strategy, the table 2 may be looked up toobtain the fourth and fifth initial recommendation results, the table 3may be then looked up to obtain the weights C4 and C5 corresponding tothe fourth and fifth initial recommendation results respectively, andthen, the fourth and fifth initial recommendation results may be fusedaccording to the corresponding weights C4 and C5 to obtain the targetrecommendation result.

In another exemplary embodiment, when the sixth recommendation strategyis the target recommendation strategy, the table 2 may be looked up toobtain the fifth initial recommendation result, the table 3 may be thenlooked up to obtain the weight C5 corresponding to the fifth initialrecommendation result, and then, the fifth initial recommendation resultmay be fused according to the weight C5 thereof to obtain the targetrecommendation result.

In this embodiment, the target recommendation parameter corresponding tothe page identifier of the page is determined according to the pageidentifier; the corresponding target recommendation strategy isdetermined according to the target recommendation parameter, and the atleast one initial recommendation result is obtained according to thetarget recommendation strategy; the at least one initial recommendationresult is fused according to the corresponding weight to obtain thetarget recommendation result. Since the target recommendation parametermay be determined according to the page, the target recommendationstrategy may be determined according to the target recommendationparameter, the at least one initial recommendation result may bedetermined according to the target recommendation strategy, and the atleast one initial recommendation result may be fused according to thecorresponding weight to obtain the target recommendation result,pertinence of information recommendation may be improved.

As shown in FIG. 3, at least one embodiment of the present disclosurefurther provides an information recommendation device, which includes:

a first determining module 31, configured for determining a targetrecommendation parameter corresponding to a page identifier of a pageaccording to the page identifier and a correspondence between pageidentifiers and recommendation parameters;

a second determining module 32, configured for determining acorresponding target recommendation strategy according to the targetrecommendation parameter;

a querying module 33, configured for querying a correspondence betweenrecommendation strategies and recommendation results according to thetarget recommendation strategy, so as to obtain at least one initialrecommendation result; and

a fusing module 34, configured for fusing the at least one initialrecommendation result according to a corresponding weight to obtain atarget recommendation result.

In this embodiment, the target recommendation parameter corresponding tothe page identifier of the page is determined according to the pageidentifier and a correspondence between page identifiers andrecommendation parameters; the corresponding target recommendationstrategy is determined according to the target recommendation parameter;the correspondence between the recommendation strategies and therecommendation results is queried according to the target recommendationstrategy, so as to obtain the at least one initial recommendationresult; the at least one initial recommendation result is fusedaccording to the corresponding weight to obtain the targetrecommendation result. Since the target recommendation parameter may bedetermined according to the page, the target recommendation strategy maybe determined according to the target recommendation parameter, the atleast one initial recommendation result may be determined according tothe target recommendation strategy, and the at least one initialrecommendation result may be fused according to the corresponding weightto obtain the target recommendation result, pertinence of informationrecommendation may be improved.

FIG. 4 is a block diagram of an information recommendation deviceaccording to one exemplary embodiment. For example, the device 400 maybe provided as a server or a user terminal (for example, a mobile phone,a desktop computer, a tablet computer, a notebook computer, etc.).Referring to FIG. 4, the device 400 includes a processing assembly 422and a memory resource represented by a memory 432, the processingassembly 422 further includes one or more processors, and the memory 432is configured to store instructions, such as an application, which areexecutable by the processing assembly 422. The application stored in thememory 432 may include one or more modules each corresponding to a setof instructions. Furthermore, the processing assembly 422 is configuredto execute the instructions to perform the above-described controlmethod of adjusting light.

The device 400 may also include a power assembly 426 configured toperform power management of the device 400, a wired or wireless networkinterface 450 configured to connect the device 400 to a network, and aninput/output (I/O) interface 458. The device 400 may be operated basedon an operating system stored in the memory 432, such as WindowsServer™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

An exemplary embodiment further provides a non-transitory computerreadable storage medium including instructions, such as the memory 432including the instructions, and the above-mentioned instructions areexecutable by the processing assembly 422 of the device 400 to performthe above-mentioned method. For example, the non-transitory computerreadable storage medium may be an ROM, a random access memory (RAM), aCD-ROM, a magnetic tape, a floppy disk, an optical data storageapparatus, or the like.

In the present disclosure, terms such as “first” and “second” are onlyused for the purpose of description and are not intended to indicate orimply relative importance. The term “a plurality of” means two or morethan two, unless specified otherwise.

The above description merely relates to exemplary embodiments of thepresent disclosure and is not intended to limit the protection scope ofthe present disclosure, which is determined by the appended claims.

1. An information recommendation method, comprising: determining a target recommendation parameter corresponding to a page identifier of a page, according to the page identifier and a correspondence between a page identifier and a recommendation parameter; determining a corresponding target recommendation strategy according to the target recommendation parameter; querying a correspondence between a recommendation strategy and a recommendation result according to the target recommendation strategy, so as to obtain at least one initial recommendation result; and fusing the at least one initial recommendation result according to a corresponding weight to obtain a target recommendation result.
 2. The information recommendation method according to claim 1, wherein the page is a first recommendation page; and the target recommendation parameter is a user identifier of a user.
 3. The information recommendation method according to claim 2, wherein the target recommendation strategy is a first recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a first initial recommendation result, a second initial recommendation result and a third initial recommendation result, according to the first recommendation strategy; wherein the first initial recommendation result is a target recommended to the user according to target preference data of the user corresponding to the user identifier; the second initial recommendation result is a target recommended to the user according to a tag of the user corresponding to the user identifier; and the third initial recommendation result is a target recommended to the user according to a put-on-sale time of the target and the user identifier, and the put-on-sale time meets a preset condition.
 4. The information recommendation method according to claim 3, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that user-target interaction behavior data corresponding to the user identifier exists in a preset database, and the target preference data is obtained according to the user-target interaction behavior data corresponding to the user identifier, and the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 5. The information recommendation method according to claim 2, wherein the target recommendation strategy is a second recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a second initial recommendation result and a third initial recommendation result, according to the second recommendation strategy; wherein the second initial recommendation result is a target recommended to the user according to a tag of the user corresponding to the user identifier; and the third initial recommendation result is a target recommended to the user according to a put-on-sale time of the target and the user identifier, and the put-on-sale time meets a preset condition.
 6. The information recommendation method according to claim 5, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that user-target interaction behavior data corresponding to the user identifier is absent in a preset database, and the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 7. The information recommendation method according to claim 1, wherein the page is a second recommendation page; and the target recommendation parameter comprises a target identifier and a user identifier of a user.
 8. The information recommendation method according to claim 7, wherein the target recommendation strategy is a third recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a first initial recommendation result, a fourth initial recommendation result and a fifth initial recommendation result, according to the third recommendation strategy; wherein the first initial recommendation result is a target recommended to the user according to target preference data of the user corresponding to the user identifier; the fourth initial recommendation result is a target recommended to the user according to a rut correspondence between the target identifier and a target identifier of a similar target, and the target preference data and the first correspondence are obtained according to user-target interaction behavior data corresponding to the user identifier; and the fifth initial recommendation result is a target recommended to the user according to the target identifier and a second correspondence between the target identifier and a target identifier of a similar target, and the second correspondence is obtained by calculating a similarity between targets according to attribute data of the targets; and the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 9. The information recommendation method according to claim 8, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that the user-target interaction behavior data corresponding to the user identifier exists in a preset database; and determining according to the target identifier that target interaction behavior data corresponding to the target identifier exists in a preset database, and the target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 10. The information recommendation method according to claim 7, wherein the target recommendation strategy is a fourth recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a first initial recommendation result and a fifth initial recommendation result, according to the fourth recommendation strategy; wherein the first initial recommendation result is a target recommended to the user according to target preference data of the user, and the target preference data is obtained by inputting user-target interaction behavior data into a trained recommendation model; and the fifth initial recommendation result is a target recommended to the user according to the target identifier and a second correspondence between the target identifier and a target identifier of a similar target, and the second correspondence is obtained by calculating a similarity between targets according to attribute data of the targets; and the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 11. The information recommendation method according to claim 10, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that the user-target interaction behavior data corresponding to the user identifier exists in a preset database; and determining according to the target identifier that target interaction behavior data corresponding to the target identifier is absent in a preset database, and the target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browning behavior data and target pushing behavior data.
 12. The information recommendation method according to claim 7, wherein the target recommendation strategy is a fifth recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a fourth initial recommendation result and a fifth initial recommendation result, according to the fifth recommendation strategy; wherein the fourth initial recommendation result is the target recommended to the user according to a first correspondence between the target identifier and a target identifier of a similar target, and the first correspondence is obtained according to user-target interaction behavior data corresponding to the user identifier; and the fifth initial recommendation result is a target recommended to the user according to the target identifier and a second correspondence between the target identifier and a target identifier of a similar target, and the second correspondence is obtained by calculating a similarity between targets according to attribute data of the targets; and the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target like-giving behavior data, target browsing behavior data and target pushing behavior data.
 13. The information recommendation method according to claim 12, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that user-target interaction behavior data corresponding to the user identifier is absent in a preset database; and determining according to the target identifier that target interaction behavior data corresponding to the target identifier exists in a preset database, and the target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data.
 14. The information recommendation method according to claim 7, wherein the target recommendation strategy is a sixth recommendation strategy; the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining a fifth initial recommendation result according to the sixth recommendation strategy; wherein the fifth initial recommendation result is a target recommended to the user according to the target identifier and a second correspondence between the target identifier and a target identifier of a similar target, and the second correspondence is obtained by calculating a similarity between targets according to attribute data of the targets.
 15. The information recommendation method according to claim 14, wherein prior to determining the corresponding target recommendation strategy according to the target recommendation parameter, the information recommendation method further comprises: determining according to the user identifier that user-target interaction behavior data corresponding to the user identifier is absent in a preset database; and determining according to the target identifier that target interaction behavior data corresponding to the target identifier is absent in a preset database, the user-target interaction behavior data comprises at least one of a group consisting of target purchase behavior data, target commenting behavior data, target sharing behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data, and the target interaction behavior data comprises at least one of a group consisting target purchase behavior data, target commenting behavior data, target haring behavior data, target collecting behavior data, target likes-giving behavior data, target browsing behavior data and target pushing behavior data. 16-17. (canceled)
 18. The information recommendation method according to claim 1, wherein the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining the at least one initial recommendation result from a database according to the target recommendation strategy, wherein the at least one initial recommendation result is stored in the database in advance.
 19. An information recommendation device, comprising: a first determining circuit configured to determine a target recommendation parameter corresponding to a page identifier of a page, according to the page identifier and a correspondence between a page identifier and a recommendation parameter; a second determining circuit configured to determine a corresponding target recommendation strategy according to the target recommendation parameter; a querying circuit configured to query a correspondence between a recommendation strategy and a recommendation result according to the target recommendation strategy, so as to obtain at least one initial recommendation result; and a fusing circuit configured to fuse the at least one initial recommendation result according to a corresponding weight to obtain a target recommendation result.
 20. An information recommendation device, comprising: a processor; and a memory, wherein the memory is configured to store instructions, and the instructions, when executed by the processor, cause the processor to execute operations comprising: determining a target recommendation parameter corresponding to a page identifier of a page, according to the page identifier and a correspondence between a page identifier and a recommendation parameter; determining a corresponding target recommendation strategy according to the target recommendation parameter; querying a correspondence between a recommendation strategy and a recommendation result according to the target recommendation strategy, so as to obtain at least one initial recommendation result; and fusing the at least one initial recommendation result according to a corresponding weight to obtain a target recommendation result.
 21. A non-transitory computer storage medium configured to store instructions, the instructions, when executed by a processor, causing the processor to execute the information recommendation method according to claim
 1. 22. The information recommendation method according to claim 2, wherein the querying the correspondence between the recommendation strategy and the recommendation result according to the target recommendation strategy, so as to obtain the at least one initial recommendation result comprises: obtaining the at least one initial recommendation result from a database according to the target recommendation strategy, wherein the at least one initial recommendation result is stored in the database in advance. 